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@brucelsuarez: Does this mean the Alphafly 4’s are coming?!? (I hope so) #nike #Running #run #vaporfly #alphafly @Nike

Bruce
Bruce
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Region: US
Tuesday 01 September 2026 21:01:40 GMT
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Comments

gabe2_ss
Gabe2_SS :
After 2 runs btw
2026-09-03 05:25:12
25
absolute0061
Absolute :
Honestly, I think it means Adidas is trouncing them and they have to move to a new price point to compete.
2026-09-03 02:29:35
9
100millionbillionn
100millionbillion :
They’re probably gonna release another generation soon
2026-09-03 01:06:34
46
built.byclutch
Teddy James :
Grabbed the yellow ones last week
2026-09-03 10:23:13
5
slavicsweat615
Zane | PTAandPRs 👨🏼‍🏫 :
UGH FINALLY!
2026-09-01 23:48:08
5
nickmendozza
Nick Mendoza :
Does legends Nike have these sales
2026-09-03 16:23:30
1
lewmoney86
lewmoney86 :
Which Nike outlet is this
2026-09-02 20:35:04
1
cesarb99
cesarbarragan99 :
Nike: let’s put these on sale so we can drop 3 new alphaflys 3 colorways😎
2026-09-02 20:48:04
1
matt_henery
matt_henery :
Oh I for sure snatched up 2 pairs!!
2026-09-03 00:10:10
1
haden.dillard0
✝️Haden⚾️ :
I got mine from the tn outlet and for $100
2026-09-03 13:04:15
0
yoambular
YoAmbular 🫥 :
Don’t forget to check the wall shoes, I found vaporflys for an additional 30% off. But they sadly were the wrong size
2026-09-03 02:11:49
1
kij.lifts
kiwi 🥝 :
👀
2026-09-02 22:02:11
1
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We’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean. The Millennium Prize Problems⁠ represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years. A major goal of our work is to empower scientists to advance research and technology that benefits all of humanity. To solve the Navier–Stokes problem, we used an internal model that is significantly more capable than GPT‑6 Astra. We believe it is important to inform the world about the pace of AI progress and what to expect from upcoming models. And we congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work. We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs. unforced).
We’re sharing a solution to the Navier–Stokes existence and smoothness problem, one of the Millennium Prize Problems. This proof, produced by an internal OpenAI system, shows that the dynamics of the Navier-Stokes equations for fluid motion can develop a singularity in finite time. We’re sharing both a writeup of the proof and a formalization in Lean. The Millennium Prize Problems⁠ represent some of the deepest questions at the frontier of mathematics. The question of whether smooth three-dimensional fluid motion can break down has remained unresolved for roughly 90 years. A major goal of our work is to empower scientists to advance research and technology that benefits all of humanity. To solve the Navier–Stokes problem, we used an internal model that is significantly more capable than GPT‑6 Astra. We believe it is important to inform the world about the pace of AI progress and what to expect from upcoming models. And we congratulate Levent Alpöge and Tristan Buckmaster on their remarkable mathematical work. We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models. However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs. unforced).

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